Voltage waveform transient identification and autonomous load coordination

ABSTRACT

Described are systems and techniques for extracting frequency and voltage harmonic transients corresponding to individual load events. Such systems and techniques can be used to make electrical loads aware of the operation of other loads in an electric grid. Thus, awareness is achieved using information derived only from a utility voltage waveform at a load. Also described are systems and techniques for incorporating such awareness into load controllers which allows loads to autonomously meet system level objectives in addition to their individual requirements.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application Nos.62/805,626 filed Feb. 14, 2019 and 62/895,240 filed Sep. 3, 2019, theentire contents of which are each hereby incorporated herein byreference in their entireties.

Government Rights

Co-inventor Spencer C. Shabshab contributed to this invention ongovernment time while he was an employee of the Department of the Navy.The Government of the United States of America has certain rights in theinvention.

BACKGROUND

As is known in the art, electric loads typically operate withuncoordinated schedules. This may result in an electrical grid to whichthe loads are coupled becoming unstable and/or unreliable and inparticular may result in unstable/unreliable power from a portion of agrid or from a microgrid. To track power quality, determine whenemergency load shedding is required to maintain grid (and/or microgrid)stability, and to detect so-called microgrid islanding events,measurements of grid voltage have been used. Most nonintrusive monitors,and power meters in general, measure both current and voltage. Powerquality disturbances have also been used to identify loads on a powersystem.

As is also known, large power consuming loads are targets forenergy-saving demand response schemes. Loads amenable to demand responsescheduling include deferrable loads, thermal loads that can be curtailedwithin acceptable temperature bounds, and curtailable loads, which canbe completely switched off until needed. Conventional demand responseschemes often require centralized optimization based upon aggregatedinputs and knowledge of load type to produce distributed tasking ordersor load scheduling across energy consumers.

SUMMARY

This Summary is provided to introduce a selection of concepts insimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key or essentialfeatures or combinations of the claimed subject matter, nor is itintended to be used to limit the scope of the claimed subject matter.

In general, described herein are systems and techniques for extractingfrequency and voltage harmonic transients corresponding to individualload events. Such systems and techniques can be used to make electricalloads “aware” of the operation of other loads in an electric grid. Asused in this context, the term “aware” is understood to mean that oneelectrical load is informed or otherwise alerted to some informationabout the other loads (e.g. whether another electrical load is starting,stopping or operating. Awareness is achieved in one embodiment usinginformation derived only from a utility voltage waveform at a load. Forexample. information extracted from frequency and voltage harmonictransients. However, “awareness” could also be achieved by intentionalsignaling means, including the use of networking, power line carriertransmissions, wireless signals, optical signals, acoustic signals, orother signaling means as would be known to a person of ordinary skill inthe art.

Also described are systems and techniques for incorporating suchawareness into load controllers which allows loads to autonomously meetsystem level objectives in addition to their individual requirements.

In one aspect, unlike prior art systems and methods for analysis ofvoltage measurements local to a load, described herein are transientidentification techniques which take into account frequency variationsin a voltage waveform as well as harmonics to identify a transient. Inembodiments, the systems and techniques described herein make explicituse of transient exemplars in a frequency waveform.

In one aspect, described are systems and techniques for creating“self-driving” loads that are aware of the operation of other localloads. Particularly on a microgrid, important energy consumers (i.e.electric loads which consume a relatively large amount of energycompared with other loads couple to the same grid) can detect theoperation of neighboring loads without special information or controlwiring. Analogous to the way a good driver is aware of neighboring cars,described herein are “autonomous demand response” schemes that permitloads to naturally optimize the overall energy consumption of anaggregate group of loads on a grid. The techniques described herein maybe utilized in microgrids and can also be applied on subsections of aconventional utility or other power system.

In accordance with one aspect of the concepts described herein, it hasbeen recognized that examination of frequency variation and harmonicsmay result in effective transient identification in practicalapplications.

It is noted that in prior art techniques, this information (i.e.information related to frequency variation and harmonics) waseffectively not considered or at least not explicitly considered.

In one aspect, described are techniques for identifying load operation“signatures” strictly from measurements of utility voltage. Thesetechniques are particularly applicable in low-inertia microgrids or“street-level” distribution networks where a utility may not be “stiff”due to generator limitations or impedance from a local distributiontransformer. Among other uses, this information could be used forautonomous control of cyclically operating loads.

In embodiments, multiple transient exemplars are introduced for a load,to account for different grid configurations that lead to differenttransient shapes (i.e. different signatures) for the same load.

In accordance with a further aspect of the concepts described herein,systems and techniques are described for autonomously identifying loadoperating information (e.g. neighboring load operating information)using voltage values local to a load. In embodiments, local voltagemeasurements are made and analyzed to determine load operatinginformation. In embodiments, analyzing voltage values local to a loadmay be used to determine load operating information of neighboringloads.

In embodiments, the system includes a nonintrusive system and techniqueto detect power events and identify them strictly from changes in avoltage waveform of the utility. Voltage-only load identification meansthat any load on a local utility network can, in principle, identify theoperating schedule of loads in the network neighborhood. With thisapproach, the utility itself effectively doubles as a load-monitoringinformation exchange network. The described system and technique can beaugmented or enhanced, where appropriate, with other signals, includingpower line carrier signals, to confirm or ensure the detection of loadoperation. This information could be used by “aware” loads toself-schedule their operation to avoid power demand peaks.

In accordance with a further aspect of the concepts described herein, anautonomous load comprises: a detection circuit configured to measure,sense or otherwise detect a characteristic of a signal provided to theautonomous load, an event identification processor for extracting andidentifying a load event based upon signal measured, sensed or otherwisedetected by the detection circuit; and an autonomous load controllerconfigured to receive load event information from the eventidentification processor and for providing a control signal to theautonomous load in response to the load event information.

With this particular arrangement, an autonomous load capable of changingits operating state in response to load event information is provided.For example, in response to receiving load event information whichindicates that another load coupled to the same electrical grid as theautonomous load has changed its state (e.g. the other load has turnedon, turned, off, slowed down, sped up, is drawing more or less powerfrom a grid, etc. . . . ) the autonomous load can change its stateaccordingly. For example, in response to an autonomous load receivingload event information which indicates that another load has changed itsstate from off to on, the autonomous load may change its state from onto off. In this example, the autonomous load may change its state to offto prevent drawing more power than can be sourced by the electrical gridto which both loads are coupled.

In embodiments, the detection circuit is configured to measure, detector otherwise sense, a characteristic of one or more signals provided tothe autonomous load. In embodiments, the detection circuit is configuredto measure, detect or otherwise sense one or more of: a power signal, avoltage signal or a current signal. In embodiments, the one or moresignals measured, detected or otherwise sensed by the detection circuitmay be one or more an analog signals, one or more digital signals or acombination of one or more analog and digital signals. In embodiments,the detection circuit may be configured to measure, detect or otherwisesense signal characteristics including but not limited to: signaltransients (including characteristics of amplitude and frequencytransients); changes in signal transients; signal frequencies and/oramplitudes; changes in signal frequencies and/or amplitudes, signalwaveforms; changes in signal waveforms; power levels (e.g. minimum,average and/or peak power levels); and/or changes in power levels.

In embodiments, the event identification processor comprises a transientidentification processor configured to extract information fromtransient characteristics of the signal measured or otherwise obtainedby the detection circuit.

In embodiments, the autonomous load may further comprise communicationmeans for signaling or otherwise communicating with other autonomousloads or other systems such as other control systems. In embodiments,such communication means may be implemented as power line carriercommunication system, a wire-based communication system (e.g. telephonenetworks, or ethernet networks, and fiber-optic networks, waveguidetransmission lines), or wireless communication system (e.g. a cellulartransceiver or other type of transceiver for performing packet-basedcommunication). Other forms of signaling may also be used.

In embodiments the autonomous load may include any one or all of thefollowing features single or in combination including, but not limitedto: the detection circuit or autonomous load controller monitors anenvironment of the load; the autonomous load controller updatesknowledge of a grid state and/or a load state; the autonomous loadcontroller causes a load to change states; the autonomous loadcontroller receives information on an operational state of a load and onexternal factors of the load and processes the operational state andexternal factor information in determining whether to cause a load tochange states; an event identification routine is triggered when anevent is detected in either or both of a frequency or a harmonic datastreams; wherein in response to the event identification processoridentifying an event as a power source on/off event or a load on/offevent, the event identification processor updates the autonomous loadcontroller's knowledge of the grid state; wherein in response to theevent identification processor not recognizing an event, the autonomousload (e.g. via the event identification processor or another processor)declares an event collision; wherein the event identification processoris configured to identify a 7th frequency harmonic characteristic.

In accordance with a further aspect of the concepts described herein, anautonomous load comprises: a voltage detection circuit coupled tomeasure a voltage signal at an input of the autonomous load; a transientidentification processor for extracting frequency and voltage harmonictransients and identifying individual load events corresponding to theextracted frequency and voltage harmonic transients; and an autonomousload controller configured to receive load event information from thetransient identification processor and for providing a control signal tothe autonomous load in response to the load event information.

In embodiments the autonomous load may include any one or all of thefollowing features single or in combination including, but not limitedto: the voltage detection circuit or autonomous load controller monitorsan environment of the load; the autonomous load controller updatesknowledge of a grid state and/or a load state; the autonomous loadcontroller causes a load to change states; the autonomous loadcontroller receives information on an operational state of a load and onexternal factors of the load and processes the operational state andexternal factor information in determining whether to cause a load tochange states; a transient identification routine is triggered when anevent is detected in either or both of a frequency or a harmonic datastreams; wherein in response to a transient identification processoridentifying an event as a power source on/off event or a load on/offevent, the transient identification processor updates the autonomousload controller's knowledge of the grid state; wherein in response tothe transient identification processor not recognizing an event, theautonomous load (e.g. via the transient identification processor)declares an event collision; wherein the transient identificationprocessor is configured to identify a 7th frequency harmoniccharacteristic.

In accordance with a further aspect of the concepts described herein, amethod comprises: detecting a signal at an autonomous load, extractinginformation from the detected signal frequency and voltage harmonictransients from the detected voltage signal, based upon the informationextracted from the detected signal, identifying an individual load eventcorresponding to the extracted frequency and voltage harmonictransients, and in response to the identified individual load event,providing one or more control signals to the autonomous load.

With this particular arrangement, a method for controlling an autonomousload is provided.

In embodiments, extracting information from the detected signalcomprises extracting frequency and harmonic transients from the detectedsignal. In embodiments, detecting signal comprises detecting a voltagesignal at an input of the autonomous load and extracting informationfrom the detected signal comprises extracting frequency and voltageharmonic transients from the detected voltage signal.

In embodiments, based upon the information extracted from the detectedsignal, comprises extracting frequency and voltage harmonic transientsfrom a detected voltage signal and identifying an individual load eventcorresponding to the extracted information comprises identifying anindividual load event corresponding to the extracted frequency andvoltage harmonic transients of the detected voltage signal.

In embodiments, the method further comprises monitoring an environmentof the load.

In embodiments, the method further comprises updating autonomous loadknowledge of a grid state and/or a load state.

In embodiments, the method further comprises causing a load to changestates.

In embodiments, the method further comprises receiving information on anoperational state of a load and on external factors of the load andprocesses the operational state and external factor information indetermining whether to cause a load to change states.

In embodiments, the method further comprises performing a transientidentification process in response to detection of an event in either orboth of a frequency or a 7th harmonic data streams.

In embodiments, in response to a transient identification processidentifying an event as a power source on/off event or load on/offevent, updating the autonomous load's knowledge of the grid state.

In accordance with a further aspect of the concepts described herein, asystem comprises an electrical power grid, a bank of power sourcescoupled to the electrical power grid and one or more autonomous loadscoupled to the electrical power grid, each of the one or more autonomousloads configured to draw power from the electrical power grid and eachof the one or more autonomous loads comprising: a detection circuitconfigured to measure, sense or otherwise detect a characteristic of asignal provided to the autonomous load, an event identificationprocessor for extracting and identifying a load event based upon signalmeasured, sensed or otherwise detected by the detection circuit; and anautonomous load controller configured to receive load event informationfrom the event identification processor and for providing a controlsignal to the autonomous load in response to the load event information.

With this particular arrangement, an autonomous load capable of changingits operating state in response to load event information is provided.For example, in response to receiving load event information whichindicates that another load coupled to the same electrical grid as theautonomous load has changed its state (e.g. the other load has turnedon, turned, off, slowed down, sped up, is drawing more or less powerfrom a grid, etc. . . . ) the autonomous load can change its stateaccordingly. For example, in response to an autonomous load receivingload event information which indicates that another load has changed itsstate from off to on, the autonomous load may change its state from onto off. In this example, the autonomous load may change its state to offto prevent drawing more power than can be sourced by the electrical gridto which both loads are coupled.

In embodiments, the detection circuit is configured to measure, detector otherwise sense, a characteristic of one or more signals provided tothe autonomous load. In embodiments, the detection circuit is configuredto measure, detect or otherwise sense one or more of: a power signal, avoltage signal or a current signal. In embodiments, the one or moresignals measured, detected or otherwise sensed by the detection circuitmay be one or more an analog signals, one or more digital signals or acombination of one or more analog and digital signals. In embodiments,the detection circuit may be configured to measure, detect or otherwisesense signal characteristics including but not limited to: signaltransients (including characteristics of amplitude and frequencytransients); changes in signal transients; signal frequencies and/oramplitudes; changes in signal frequencies and/or amplitudes, signalwaveforms; changes in signal waveforms; power levels (e.g. minimum,average and/or peak power levels); and/or changes in power levels.

In embodiments, the event identification processor comprises a transientidentification processor configured to extract information fromtransient characteristics of the signal measured or otherwise obtainedby the detection circuit.

In embodiments, the autonomous load may further comprise communicationmeans for signaling or otherwise communicating with other autonomousloads or other systems such as other control systems. In embodiments,such communication means may be implemented as power line carriercommunication system, a wire-based communication system (e.g. telephonenetworks, or ethernet networks, and fiber-optic networks, waveguidetransmission lines), or wireless communication system (e.g. a cellulartransceiver or other type of transceiver for performing packet-basedcommunication). Other forms of signaling may also be used.

In embodiments, the one or more autonomous loads may comprise a voltagedetection circuit configured to measure voltage values at an input to aload, a transient identification processor configured to receive themeasured voltage values from the voltage detection circuit andconfigured to process the voltage values to identify frequencyvariations and harmonics in the voltage signals to identify a transientand to compare the transient to an exemplar.

In embodiments, the power source bank comprises a plurality ofgenerators.

In embodiments, the power sources may be provided as one or more of fueloperated generators, solar generators and wind power turbines.

In embodiments, the electrical power grid corresponds to a microgrid.

In embodiments, the autonomous load controller monitors one or both ofan external or an internal environment of the load.

In embodiments, the autonomous load controller updates knowledge of agrid state and/or a load state.

In embodiments, the autonomous load controller causes a load to changestates.

In embodiments, the autonomous load controller receives information onan operational state of a load and on external factors of the load andprocesses the operational state and external factor information indetermining whether to cause a load to change states.

In embodiments, a transient identification routine is triggered inresponse to an event being detected in either or both of a frequencydata stream or a harmonic data stream.

In embodiments, the harmonic data stream is a 7th harmonic data stream.

In embodiments, in response to the transient identification processoridentifying an event as a power source on/off event or load on/offevent, the transient identification processor updates the autonomousload controller's knowledge of the grid state.

In embodiments, in response to the transient identification processornot recognizing an event, the transient identification processordeclares an event collision.

In accordance with a still further aspect of the concepts describedherein, an autonomous load comprises: a voltage detection circuitcoupled to measure a voltage signal at the input of the autonomous load,a transient identification processor for extracting frequency andvoltage harmonic transients and identifying an individual load eventscorresponding to the extracted frequency and voltage harmonictransients; and an autonomous load controller configured to receive loadevent information from the transient identification processor and forproviding a control signal to the autonomous load in response to theload event information.

With this particular arrangement, an autonomous load capable of changingits operating state in response to load event information is provided.For example, in response to receiving load event information whichindicates that another load coupled to the same electrical grid as theautonomous load has changed its state (e.g. the other load has turnedon, turned, off, slowed down, sped up, is drawing more or less powerfrom a grid, etc. . . . ) the autonomous load can change its stateaccordingly. For example, in response to an autonomous load receivingload event information which indicates that another load has changed itsstate from off to on, the autonomous load may change its state from onto off. In this example, the autonomous load may change its state to offto prevent drawing more power than can be sourced by the electrical gridto which both loads are coupled.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages will beapparent from the following more particular description of theembodiments, as illustrated in the accompanying drawings in which likereference characters refer to the same parts throughout the differentviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating the principles of the embodiments.

FIG. 1 is a block diagram of a system comprising a transientidentification processor;

FIG. 2 is a flow diagram of an illustrative process for identifying avoltage waveform transient;

FIG. 3 is a block diagram of signal processor appropriate for use insystems such as those shown in any of FIGS. 1, 4, 5 and 21 forperforming a signal extraction process from voltage measurements;

FIG. 4. is a block diagram of a system comprising multiple power sourcescoupled to one or more loads through a microgrid;

FIG. 5 is a bock diagram of a system comprising one or more powersources coupled through a grid to one or more electrical loads with atleast one load having a transient identification processor coupledthereto;

FIG. 6 is a plot of maximum frequency deflection (Hz) vs. a base load(kW);

FIG. 7 is a plot of frequency transients (Hz) vs. time (seconds);

FIG. 7A is a plot of frequency transients (Hz) vs. time (seconds);

FIG. 8 is a plot of maximum frequency deflection (Hz) vs. a base load(kW);

FIGS. 9-9B are a series of plots illustrating frequency deviation (Hz)vs. time (seconds);

FIGS. 10 and 10A are a series of plots illustrating frequency deviation(Hz) vs. time (seconds);

FIG. 11. is a schematic diagram of an approximate seventh harmoniccircuit model for a microgrid which may be the same as or similar to themicrogrid of FIG. 5;

FIG. 12 is a plot of an in-phase 7th harmonic (volts) vs. normalizedload;

FIG. 13 is a plot of an in-phase 7th harmonic values (volts) vs.normalized load;

FIGS. 14-14B are a series of plots illustrating in-phase 7th harmonicvalues (volts) vs. time (seconds);

FIGS. 15 and 15A are a series of plots illustrating in-phase 7thharmonic values (volts) vs. time (seconds);

FIG. 16 is a diagram illustrating an exemplar management process;

FIG. 17 is a bock diagram illustrating a matching technique fortransient identification;

FIG. 18 is a bar chart illustrating energy consumption as a percent oftotal energy consumption for three different loads;

FIG. 19 is a plot of system power draw (kW) vs. time (minutes);

FIG. 19A is a plot of efficiency (%) vs. load (kW) for the system powerdraw of shown in FIG. 19 for one, two and three generator systems;

FIG. 20 is a plot of a number of power sources and loads online vs. time(minutes);

FIG. 20A is a plot of frequency (Hz) vs. time (minutes) for the onlinepower sources and loads online shown in FIG. 20;

FIG. 20B is a plot of in-phase 7th harmonic values (volts) vs. time(minutes) for the frequencies shown in FIG. 20A;

FIG. 21 is a block diagram of one or more an autonomous loads coupled toa power grid;

FIG. 22 is a state diagram of an example heater;

FIG. 23 is a flow diagram of an illustrative under-temperature processfor a heater having a state diagram as shown in FIG. 22;

FIG. 24 is a flow diagram of an over-temperature / overlength processfor a heater having a state diagram as shown in FIG. 22;

FIG. 25 is a flow diagram of an illustrative transient identificationprocess for a heater having a state diagram as shown in FIG. 22;

FIG. 26 is a flow diagram of an illustrative collision-handling processfor a heater having a state diagram as shown in FIG. 22; and

FIG. 27 is a flow diagram of an illustrative collision-handling processfor a heater having a state diagram as shown in FIG. 22.

DETAILED DESCRIPTION

Referring now to FIG. 1, a power source 10 generates a power which iscoupled or otherwise provided to a grid 12. Power source 10 may beprovided as any type of power source capable of being coupled to grid12. For example, power source 10 may be provided as any of a generatorincluding, but not limited to a fossil fuel powered generator, a solarpowered generator, a wind powered generator a hydro-powered generator, afuel-cell with power electronics, a battery with power electronics, orother sources of electrical energy and storage.

It should be noted that grid 12 may correspond to a portion of a largerelectrical grid system or may correspond to a microgrid. A load 14coupled to grid 12 draws power from the grid. A voltage detectioncircuit 16 is coupled to non-intrusively measure or otherwise detectvalues of a voltage (e.g. a utility voltage) received by load 14. Suchdetected voltage values (which may be analog signals or digital datavalues) are provided to an input of a transient identification processor18.

Transient identification processor receives the signals provided theretoand processes the signals to identify load operation characteristics or“signatures.” Thus, in embodiments, strictly from measurements of autility voltage and using the techniques described in detail hereinbelow, transient identification processor 18 is able to identify loadoperation characteristics. Techniques used by transient identificationprocessor 18 to identify load signatures are described hereinbelow inconjunction with at least FIGS. 3 and 6-17.

By identifying load signatures, transient identification processor isable to detect power events and identify them strictly from changes in avoltage waveform of the utility. Voltage-only load identification meansthat any load on a local utility network coupled to a transientidentification processor can, in principle, identify the operatingschedule of loads in the network neighborhood. With this approach, theutility itself effectively doubles as a load-monitoring informationexchange network.

The signature identification techniques utilized by transientidentification processor and described herein are particularlyapplicable in low-inertia microgrids or “street-level” distributionnetworks where a utility may not be “stiff” due to generator limitationsor impedance from a local distribution transformer, for example.

Among other uses, such signature information could be used for controlof loads coupled to the grid including, but not limited to, cyclicallyoperating loads (i.e. load which sporadically or periodically draw powerfrom the grid) and high demand loads (i.e. loads, which when operating,draw an amount of power from the grid which is relatively large comparedwith the amount of power drawn by other loads on the grid or which isrelatively large compared with the amount of power available from thegrid).

In embodiments, transient identification processor 18 may thus beoptionally be coupled 20 to load 14 or to another device (not shown)such as a processing device which may be separate or integral with theload. Thus, information determined by transient identification processor18 may be utilized to control aspects of the operation of load 14 or tocontrol other aspects of the power source 10 or the grid 12.

In embodiments, load 14 may be a conventional electrical load havingvoltage detection circuit 16 and transient identification processor 18coupled thereto. In embodiments, one or both of voltage detectioncircuit 16 and transient identification processor 18 may be separatefrom load 14. In embodiments, one or both of voltage detection circuit16 and transient identification processor 18 may be provided as anintegral part of load 14.

FIG. 2 is a flow diagram showing illustrative processing that can beimplemented within an autonomous load as discussed above and, moreparticularly, within a transient identification processor, such as thetransient identification processor 18 described above in conjunctionwith FIG. 1. Rectangular elements (typified by element 22 in FIG. 2),are herein denoted “processing blocks,” and represent computer softwareinstructions or groups of instructions. Alternatively, the processingblocks may represent steps or processes performed by functionallyequivalent circuits such as a digital signal processor circuit or anapplication specific integrated circuit (ASIC). The flow diagram doesnot depict the syntax of any particular programming language, but ratherillustrates the functional information one of ordinary skill in the artrequires to fabricate circuits or to generate computer software toperform the processing described. It should be noted that many routineprogram elements, such as initialization of loops and variables and theuse of temporary variables are not shown. It will be appreciated bythose of ordinary skill in the art that unless otherwise indicatedherein, the particular sequence of blocks described is illustrative onlyand can be varied without departing from the spirit of the concepts,structures, and techniques sought to be protected herein. Thus, unlessotherwise stated the blocks described below are unordered, meaning that,when possible, the functions represented by the blocks can be performedin any convenient or desirable order.

Turning now to FIG. 2, as shown in processing block 22, a transientidentification technique begins by detecting or otherwise receiving,sensing or identifying a signal waveform such as a voltage waveform.Processing them proceeds to processing block 24 in which frequencyvariation characteristics of the signal waveform are identified. Suchharmonics may be viewed and/or identified, for example, on spectrumanalyzer or other type of signal analyzers.

Processing then proceeds to processing 26 in which harmonics in thefrequency characteristics of the signal waveform are identified. Basedupon the frequency harmonic signals, the system identifies a transientas shown in processing block 28.

Referring now to FIG. 3, a signature extraction pipeline 30 which may,for example, be implemented as part of a transient identificationprocessor such as transient identification processor 18 described inFIG. 1, takes as input 32 a voltage signal. In embodiments, the input 32may be provided as a single-phase voltage. In embodiments, the input 32may be provided as a multi-phase voltage. In embodiments, the voltagesignal, may be provided as digital data such as one or more digitalbits. In embodiments, the digital data may be provided as a sequence orstream of digital bits denoted v[n], where n is an integer andrepresents a sample index such that v[1] (i.e. n=1) corresponds to thefirst sample of voltage signal v. In embodiments, the digital data isthe sampled with frequency, f_(s). The stream of digital bits v[n], areprocessed to produce time-series “streams” of frequency estimates(denoted {tilde over (f)}[m]), and voltage harmonic coefficientestimates(denoted

_(k) k [m]), at a rate double the line frequency, i.e., the rate ofzero-crossings in the line voltage.

As shown in FIG. 3, from the digital data stream v[n],the process firstestimates the times of voltage zero-crossings via a zero crossingdetector (ZCD) module 34. In one embodiment, this may be accomplished byinterpolating between the voltage data points just before, v[n⁻ _(m)],and just after, v[n⁺ _(m)], the m^(th) zero crossing. Any type ofinterpolation technique may be used. In embodiments in which the ZCDlinearly interpolates between data points, zero-crossing stream data{circumflex over (t)}_(z)[m] may be computed as:

$\begin{matrix}{{{\hat{t}}_{z}\lbrack m\rbrack} = {{t\left\lbrack n_{m}^{-} \right\rbrack} - {\frac{v\left\lbrack n_{m}^{-} \right\rbrack}{f_{s}\left( {{v\left\lbrack n_{m}^{+} \right\rbrack} - {v\left\lbrack n_{m}^{-} \right\rbrack}} \right)}.}}} & (1)\end{matrix}$

in which:

-   -   m is an index representing a number of a zero crossing;    -   t[n⁻ _(m)] is a time of voltage zero crossing at a voltage data        points just before the m^(th) zero crossing;    -   v[n⁻ _(m)] is a voltage data points just before the m^(th) zero        crossing;    -   v[n⁺ _(m)] is a voltage data points just after the m^(th) zero        crossing; and    -   f_(s) is a sampling frequency.

Other interpolation techniques, may of course, also be used.

Thus, zero crossing detector 34 generates or otherwise produces a streamof data referred to as a zero-crossing stream data (denoted t_(z)[m])corresponding to the zero crossings of the sampled voltage signal. Zerocrossing detector 34 provides the zero-crossing stream data {circumflexover (t)}_(z)[m] to a section interpolator module 36. Sectioninterpolator module 36 receives the zero-crossing stream data and alsoreceives sampled voltage signal data v[n] 35. In response to the signalsprovided thereto (i.e. the {circumflex over (t)}_(z)[m] and v[n] data),the section interpolator module 36 estimates the average frequencyacross the m^(th) data section of length N_(p) voltage periods as,

$\begin{matrix}{{\hat{f}\lbrack m\rbrack} = {\frac{N_{p}}{{{\hat{t}}_{z}\left\lbrack {m + N_{p}} \right\rbrack} - {{\hat{t}}_{z}\left\lbrack {m - N_{p}} \right\rbrack}}.}} & (2)\end{matrix}$

The section interpolator module 36 then resamples the voltage waveformsection at the rate,

$\begin{matrix}{{{f_{s}^{\prime}\lbrack m\rbrack} = {\frac{N^{\prime}\lbrack m\rbrack}{N_{p}}{\hat{f}\lbrack m\rbrack}}},{where}} & (3) \\{{{N^{\prime}\lbrack m\rbrack} = \left\lceil {N_{p}\frac{f_{s}}{\hat{f}\lbrack m\rbrack}} \right\rceil},} & (4)\end{matrix}$

is the number of new samples over the N_(p) period-length section. Here,the symbols n represent the ceiling function meaning that N′[m] is theinteger value larger than the “prorated” number of samples betweenvoltage zero-crossings, m−N_(p) and m+N_(p). In this way, f′_(g)[m] isthe smallest sample rate higher than f_(s) that results in an integernumber of samples in the data section (and ideally, results in an exactinteger number of samples in the data section). This process reduces thevariance of the spectral leakage errors by aligning the frequency binsof the spectral analysis with the fundamental and harmonic frequenciesof interest.

A spectral analysis processor 38 computes or otherwise determines thecoefficients for these components. In embodiments, the coefficients forthese components may, for example, be calculated via a discrete FourierTransform (DFT), as:

$\begin{matrix}{{{\hat{V}}_{k}\lbrack m\rbrack} = {\sum\limits_{n^{\prime} = 0}^{N^{\prime} - 1}{{v_{m}\left\lbrack n^{\prime} \right\rbrack}{e^{{- j}\; 2\;\pi\;{{kn}^{\prime}/N^{\prime}}}.}}}} & (5)\end{matrix}$

In this way, signature extraction pipeline 30 produces signature streams39 comprised of a first signature stream (i.e. a frequency stream{circumflex over (f)}[m] corresponding to the average frequency acrossthe m^(th) data section of length N_(p) voltage periods), and a secondsignature stream(i.e. a harmonic stream {circumflex over (V)}_(k)[m])which may be used to assess grid events through voltage monitoring.Thus, it has been discovered that a combination of harmonic streams andfrequency streams provides unique identification of loads on many gridsincluding but not limited to microgrids.

Referring now to FIG. 4, a power source bank 40 may provide electricalservice to an electrical grid 42 (also sometimes referred to as an“electric grid,” a “power grid” or more simply a “grid”) 42. In thisillustrative embodiment, power source bank 40 is here shown to include Mpower sources denoted 40 a-40M. It should, of course, be appreciatedthat power source bank 40 may include any number of power sources (e.g.one or more power sources) and that the power sources may be any type ofelectrical power source including but not limited to fuel operatedgenerators (e.g. fossil fuel operated generators), solar generators,wind power turbines, to name but a few different types of power sources.It should also be noted that at any point in time, one, some or all ofthe M power sources may act to source electrical power to the grid 42.It should also be noted that grid 42 may correspond, for example, to aportion of a larger electrical grid system or may correspond to amicrogrid.

A plurality of loads 44 a-44N are coupled to grid 42 and are capable ofreceiving power via the grid. It should be noted that at any point intime, one, some or all of the N loads may draw power from grid 42. Eachload 44 a-44N has coupled thereto a corresponding one of a plurality ofvoltage detection circuits 46 a-46N. Voltage detection circuits arecoupled to measure or otherwise detect values of a voltage (e.g. autility voltage) provided to the corresponding load to which it iscoupled. In embodiments, the voltage detection circuits are coupled tonon-intrusively measure or otherwise detect values of a voltage (e.g. autility voltage) provided to the corresponding load. Each voltagedetection circuit 46 a-46N has coupled thereto a corresponding one of aplurality of transient identification processors 48 a-48N. Voltagevalues (which may be analog signals or digital data values) detected bythe voltage detection circuits are provided to inputs of the respectiveones of the transient identification processors.

The transient identification processors receive the signals providedthereto and process the signals to identify load operationcharacteristics or “signatures” of the respective loads. Thus, inembodiments, strictly from measurements of a utility voltage, thetransient identification processors may identify load operationcharacteristics.

Such techniques may be particularly applicable in low-inertia microgridsor “street-level” distribution networks where a utility may not be“stiff” due to generator limitations or impedance from a localdistribution transformer. Among other uses, and as will be explained indetail below in conjunction with FIGS. 18-27, this information could beused for autonomous control of loads including, but not limited tocyclically operating loads (i.e. load which sporadically or periodicallydraw power from the grid) and high demand loads (i.e. loads, which whenoperating, draw an amount of power from the grid which is relativelylarge compared with the amount of power drawing by other loads on thegrid or which is relatively large compared with the amount of poweravailable from the grid).

Transient identification processors 48 a-48N may also be coupled tocorresponding ones of loads 44 a-44N and may be configured to control atleast some operation of a load to which it is coupled. In this case(i.e. when a transient identification processors is coupled to a load)the load may be referred to as an “autonomous load” or as a“self-driving load” (i.e. a load which utilizes signature informationgenerated by a transient identification processors and uses thesignature information to operate in accordance with an autonomouscontrol scheme). Examples of autonomous loads will be described indetail below in conjunction with FIGS. 18-27.

As noted above in conjunction with FIG. 1, in embodiments, one or moreloads may be conventional electrical loads having a separate voltagedetection circuit and transient identification processor coupled thereto(i.e. one or both of voltage detection circuit and transientidentification processor may be separate from the load),In embodiments,one or more loads may have the voltage detection circuit and transientidentification processor provided as an integral part of thereof.

Referring now to FIG. 5, an illustrative islanded microgrid is poweredby a generator bank 50 (with two generators 50 a, 50 b being shown). Thegenerator bank 50 sources power to a plurality of loads 52 a-52 f. Inthis illustrative embodiment, voltage meters 54 a, 54 b are coupledrespective ones of loads 52 a, 52 b. In embodiments, the voltage metersmay be coupled to utility panels of loads 52 a, 52 b.

The loads may be provided as any type of load. Loads 52 a-52 d may, forexample, be provided as cyclical loads such as an environmental controlunit (ECU). Each load 52 a, 52 b also includes a transientidentification processor.

The responses of the line voltage to load turn-on/off events, generatordispatch/secure events (hereby referred to as generator turn-on/offevents, respectively), and the turn-on/off events of other loads may bemeasured by volt meters 54 a, 54 b and the measure data may be providedto the respective transient identification processor for processing. Inparticular the measured data may be analyzed to extract operationsignatures e.g. using the process of FIG. 3. Further, using across-correlation based transient identification technique, load eventrecognition may be performed using the extracted signatures.

To promote clarity in the description of the broad concepts sought to beprotected herein, an example embodiment will be described herein belowin which the generator and loads are described with particularity. Theexample described below is not intended to be and should not be,construed as limiting.

In one example embodiment, the generator bank 50 includes two 60 kWsynchronous diesel generators under dispatch control which source120/208V, 60 Hz service to the microgrid. Thus, in this example a bankof interconnected diesel generators that serve (i.e. provide power to)the microgrid.

The loads are provided as environmental control units (ECUs) 52 a-52 cwhich provide heating and cooling to respective one of buildings 56, 58(or sections 56 a, 56 b, 58 a, 58 b of buildings 56, 58). Each ECU 52a-52 c operates independently from other ECUs in neighboring buildings(or in different sections of the same building). Simply, the localtemperature conditions determine the activation time for each ECU.Periodically, ECU activation times can align resulting in many ECUsbeing on at the same time and a maximum demand on the utility.

In accordance with the concepts described herein, it has been recognizedthat nonintrusive voltage signals indicating the operation of localloads are most easily derived in situations where the information ismost likely to be helpful. That is, small grids or subsections of thegrid are more likely to display voltage distortions, e.g., frequencydeviations and harmonic content, that can be used as information forload control. Such distortions can be especially noticeable on islandedmicrogrids, where generation capacity and individual load demand may becomparable.

For example, if heaters within the ECU have a 10 kW rating they mayconsume approximately 17% of the rated power of a single 60 kW dieselgenerator.

On such microgrids, electrical transients result in grid frequencydeviations due to the finite bandwidth associated with synchronousgenerator field controllers and speed governors, as well as thefrequency control of power electronic inverters connecting renewables orenergy storage devices. Such sources also typically exhibit loaddependent harmonic voltage distortion, which is further exacerbated bymodern nonlinear loads whose non-sinusoidal currents manifest asvoltages as currents flow through the impedances of the grid As acorollary, frequency and harmonic deviations also occur when generatorscome on-line or go off-line to match load demand. These deviations canserve as signatures to identify the load and generation status of thegrid.

In the illustrative embodiment of FIG. 5, one or both of two generatorsmay provide 120/208V, 60 Hz service to the microgrid.

Also in the illustrative embodiment of FIG. 5, the loads are coupledradially to a point of common coupling with the generators. Inembodiments in which the loads correspond to ECUs, the ECUs may eachprovide heat to a section of a building structure. In addition to theECUs a three-phase variable-resistive load bank 52 c capable of up toapproximately 25 kW is connected to the grid. Other loads 52 f, e.g.,lighting, plug loads, pumps, may also be coupled to the grid andoperating. These loads draw much smaller currents than the ECUs and donot create significant voltage transients.

The response of the line voltage to ECU, pump, and generator events wasmeasured under a variety of base load and generator configurations.Generator events occurred according to their dispatch control ruleswhich, in this example system, dictate a second generator coming onlineafter loads in excess of 48 kW (80% single generator capacity) for 10consecutive seconds, and going offline after loads below 36 kW (60%single generator capacity) for 5 consecutive minutes. This may result innumerous (e.g. several hundred) ECU turn-on/off transients, eightgenerator turn-on transients, seven generator turn-off transients, andfour latrine pump turn-on transients over a period of time. Datacollected may be processed for event signatures.

In embodiments, the voltage meters may sample voltage at a samplingfrequency (fs) of about 8 kHz with a 10-bit resolution. The data may beanalyzed over N_(p)=6 period-length windows. In this illustrativeembodiment, It was found that N_(p)=6 period-length windows was a lengthwhich provided a good tradeoff between “smoothing” measurement andprocess noise and capturing frequency and harmonic features related togrid transients.

Spectral analysis was performed for the fundamental and the 3^(rd),5^(th), and 7^(th) harmonics, as these lower odd harmonics areoften-present artifacts of load induced and synchronous generationdistortion. Of these harmonic streams, the 7^(th) harmonic estimate,

₇[m], proved most useful and when combined with the frequency estimatestream, f[m], was sufficient for identifying all major electrical eventsin the system.

Next described are characteristics of event transient signatures. It hasbeen found that the voltage transients induced by electrical events varyin character depending upon how many power sources are operating, thetypes of power sources (e.g. fossil fuel generators vs. wind-poweredgenerators vs. solar-powered generators) and how much power they aresupplying (base load) before the transients occur. Thus, each transientcan be categorized as an “event type” based upon the load action thatcaused it, the number of generators running prior to it, and the totalbase load prior to it.

With respect to frequency signatures, it was also found that the largestloads (e.g. ECU heaters and pumps in the example system)coupled to thegrid create larger frequency deviations than other loads.

FIG. 6 shows the maximum frequency deflection as a function of base loadfor all ECU turn-on events when only one generator (e.g. one ofgenerators 50 a, 50 b in FIG. 5) was powering the grid. FIG. 6 reveals amoderate dependency between the size of the frequency transients and thebase load at the time of the ECU event. A plot of ECU turn-off eventmaximum frequency deflections would look similar in shape but reflectedabout the x-axis and shifted out approximately 10 kW as events arecategorized by the base camp draw leading up to each event.

Interestingly, ECU transients take on one of two distinct shapes whenonly 1 generator is running.

Referring now to FIGS. 7, 7A, curves 60, 62 represent each ECU turn-ontransients corresponding to a base load of approximately 13 kW. Here,the faint lines 60, 62 are the individual transients, and the blacklines 60′, 62′ are time-averaged composite waveforms (referred to hereinas an “exemplar”). Most transients look like the exemplar in FIG. 7,where the frequency deviation is arrested and brought smoothly back toits nominal value over the course of about 1.5 s. Occasionally though,the frequency is arrested and “aggressively” brought back near itsnominal value in less than 0.5 s, as shown in FIG. 7A. ECU turn-offevents create similar, but opposite in sign, transients. These differingbehaviors may be a result of time-varying and/or nonlinear control inthe generators' frequency regulators.

Referring now to FIG. 8, when both generators 50 a, 50 b operate inparallel, the peak frequency deviation of an ECU turn-on or turn-offtransient are generally smaller, but vary more significantly with baseload as shown in FIG. 8. Unlike the transients when one generator isoperating, these transients only take on a shape similar to shapeillustrated in FIG. 7.

Generator turn-on events were not found to create significant frequencytransients, with maximum frequency deflections smaller than 200 MHzregardless the base load. Generator turn-off events, however, did inducesignificant frequency transients at larger base loads, but not atsmaller ones.

FIGS. 9-9B depict example frequency deviations for base loads of 4 kW(FIGS. 9) and 31 kW (FIG. 9A). At low loads, the transient is small anddifficult to identify. However, at higher loads, generator turn-offevents produce easily detected frequency transients, but ones thatclosely resemble those induced by some ECU turn-on events when twogenerators are operating, e.g., the ECU turn-on event shown in FIG. 9B.

Similarly, pump turn-on events produce frequency transients easilyconfused with some ECU turn-on events, even though the pump consumesless than half the steady-state power of an ECU heater. However, due toits sizable inrush current, it still creates a frequency deviation ofcomparable size as shown in FIGS. 10 and 10A.

When considering these frequency transients as event signatures, theycannot, alone, provide unambiguous indication of each electrical eventtype on the microgrid. While frequency transients provide strongindications of all ECU event types, under some conditions ECU turn-onevent signatures can be very similar to generator turn-off events orlatrine pump turn-on events. Further, the frequency signaturescorresponding to generator turn-on events and those corresponding togenerator turnoff events under low base loads are only weak indicatorsof these events. Therefore, at least one additional event signaturestream must be observed for unambiguous electrical event monitoring.

The in-phase seventh harmonic voltage content, i.e.,

_(7,r)[m]=Re{

₇[m]}, was found to be particularly useful in resolving the ambiguitiesleft by the frequency transients. As described in FIG. 3, examining bothstreams (frequency and seventh harmonic voltage) together permitsunambiguous electrical event identification of grid coupled loads. Asnoted above, more generally, the techniques described herein utilizecombinations of harmonic and frequency streams to provide uniqueidentification of key loads on grids including but not limited tomicrogrids.

Referring now to FIG. 11, show is an approximate seventh harmoniccircuit model for the microgrid of FIG. 5 that is useful forunderstanding the effects of electrical events on the grid's seventhharmonic content. Here, line impedances are ignored as load andgenerator turn-on and turn-off events were found to be reasonablyuniform across all measurement points. Variable resistor R_(lb)represents the controllable load bank, and all ECU heater impedances andseventh harmonic current injections are lumped into R_(h) and I_(v,7),respectively. I_(v,7) is the sum of all the ECU ventilation fan seventhharmonic currents, and R_(h) varies with the number of energized ECUheaters such that,

$\begin{matrix}{R_{h} = {\left( {\frac{s_{1}}{R_{h\; 1}} + \frac{s_{2}}{R_{h\; 2}} + \frac{s_{3}}{R_{h\; 3}} + \frac{s_{4}}{R_{h\; 4}}} \right)^{- 1}.}} & (6)\end{matrix}$

Here, s_(n) is a binary variable equal to one when the n^(th) ECU heateris energized and zero when it is not.

V_(g,7) represents the combined seventh harmonic voltage of the twogenerators, and X_(g,7) represents their combined reactance at 420 Hz(seventh harmonic of 60 Hz). With the two generators essentiallyidentical, their combined source characteristics can be described as,

$\begin{matrix}{{{V_{g,7}\left( {P_{L},n_{g}} \right)} = {V_{{g\; 1},7}\left( \frac{P_{L}}{n_{g}P_{g,r}} \right)}}{and}} & (7) \\{{X_{g,7}\left( n_{g} \right)} = {\frac{X_{g\; 1{\ldots 7}}}{n_{g}}.}} & (8)\end{matrix}$

Here, V_(g1,7) (γ) is the seventh harmonic voltage of a single generatorunder the per generator normalized load (percent capacity),

$\begin{matrix}{{\gamma = \frac{P_{L}}{n_{g}P_{g,r}}},} & (9)\end{matrix}$

where P_(L) is the total fundamental power provided to the grid by n_(g)generators each with a capacity rating of P_(g,r) (60 kW).

Similarly, X_(g1,7) is the output reactance of a single generator.

The combined contribution of these two bulk sources to the seventhharmonic voltage at each heater is,

$\begin{matrix}{{V_{7} = {{\left( \frac{\left. R_{l\; b}||R_{h} \right.}{\left. {{jX}_{g,7} + R_{l\; b}}||R_{h} \right.} \right)V_{g,7}} + {\left( {R_{l\; b}{R_{h}}{jX}_{g,7}} \right)I_{v,7}}}},} & (10)\end{matrix}$

where the k operator indicates the parallel combination of twoimpedances. In the illustrative system, X_(g,7)<<R_(lb)∥R_(h). This islikely to also be true for most practical grids as generator reactancecharacteristics at 60 Hz need to be negligible to avoid significantvoltage droop in the system. With this the case, the seventh harmonicvoltage at each heater can be approximated as,

V ₇ ≈V _(g,7) +jX _(g,7) I _(v,7).   (11)

That is, the seventh harmonic voltage measurements at each ECU dependupon both the seventh harmonic voltage distortion of the generators andthe seventh harmonic currents injected by the three-phase rectifiers ofECU ventilation fans. Notably, X_(g,7) does not significantly affect thegenerators' seventh harmonic voltage distortion contributions. Further,V_(g,7) is a function of normalized load when combining equations (7)and (9). These relationships were confirmed through steady-state testswith all ECU ventilation fans off so as to eliminate the contribution ofI_(v,7).

Referring now to FIG. 12, a plot of in-phase 7^(th) harmonic (

_(7,r)) vs. normalized load (γ), illustrates a monotonically increasingfunction of the normalized load, γ. Both generators produce similarlevels of seventh harmonic distortion, as does their parallelcombination, even though X_(g,7) decreases when the two are paralleled.

Referring now to FIG. 13, when the ventilation fans are operating, theirseventh harmonic currents I_(v,7) interact with the generator reactancecharacteristics to shift the in-phase seventh harmonic voltages measuredat the ECUs as shown in FIG. 13. Here, all data points from FIG. 12 arerepeated for reference. This shift is more pronounced when only onegenerator is operating as X_(g,7) is twice as large compared to whenboth generators operate (assuming the generators are identical). Itshould be noted that I_(v,7) varies in time as the ventilation needs ofeach tent dictates fan speed, and so the I_(v,7) contribution to V₇ isnot necessarily constant. However, these variations generally occurslowly compared to the abrupt changes in V_(g,7) accompanying the stepsin P_(L) caused by a load turn-on or turn-off event.

This seventh harmonic voltage relationship with normalized load resolvesthe ambiguities leftover from the frequency signatures. Specifically,generator turn-on events (n_(g) increases by one), which only createsmall frequency transients, generate large step-down transients in

_(7,r) due to the corresponding decreases in X_(g,7) and γ ((8) and (9),respectively).

In FIG. 13, this is both a shift down from single generator to twogenerator data and a shift left in normalized load further decreasingthe in-phase seventh harmonic voltage. Conversely, generator turn-offevents generate large step-up transients in

_(7,r).

FIGS. 14-14B provides example

_(7,r) signatures corresponding to the same generator turn-off events ofFIGS. 9-9B. Notably, the voltage transient in FIG. 14 corresponding to agenerator turn-off at low load provides a clear event signature whereasthe frequency transient did not. Further, the size of the

_(7,r) generator turn-off transient in FIG. 14A is easilydistinguishable from the ECU turn-on event transient of the right plot,while the two corresponding frequency transients are easily confused.

Similarly, FIGS. 15 and 15A reveal that the

_(7,r) steps corresponding to the frequency transients of FIGS. 10 and10A help to distinguish between an ECU turn-on and pump turn-on, as thesize of these steps is dictated by the steady-state power demand of theloads rather than the in-rush power.

When considered together, frequency and in-phase seventh harmonicvoltage streams provide detectable and distinct transients that indicatefour important microgrid events: ECU heater on, ECU heater off,Generator on, and Generator off. To automate this detection process, acorrelation-based transient identification technique is used. Thecorrelation-based transient identification technique uses “fingerprint”exemplars, i.e. representative transient signatures, to identify events.

To generate signature exemplars, a set of 512 ECU events and 15generator events, each consisting of 1601 m-samples (approx. 13.3 s) offrequency and 7th-harmonic data containing each stream's transient werelabelled. From this set of labeled data, 268 ECU events and 11 generatorevents were selected to create event exemplars “fused” from similarlyshaped transient in this training set. Creating these exemplars allowsthe use of correlation methods for transient identification whilelimiting the number of exemplar waveforms to match against.

FIG. 16 illustrates the exemplar formulation process. As shown at 72,The exemplar creation technique receives a transient signature setlabeled with the event that caused it, the number of dispatchedgenerators leading up to the event, and the base load at which itoccurred. For each of the two streams that comprise the transientsignature, the exemplar formulation process assesses whether thetransient matches with any previously generated exemplar for that eventtype, number of generators, and base load (72). This matching isdetermined by first aligning the transient signature, y[m], with theexemplar, x[m], by index-shifting the transient by I_(o) indices where:

$\begin{matrix}{l_{o} = {\underset{l}{\arg\;\max}{\sum{{x\lbrack m\rbrack}{{y\left\lbrack {m + l} \right\rbrack}.}}}}} & (12)\end{matrix}$

Here, I_(o) is the lag corresponding to the maximum in thecross-correlation of the two waveforms. The match is then scored as,

$\begin{matrix}{S = \frac{\sum\left( {{x\lbrack m\rbrack} - {y\left\lbrack {m + l_{o}} \right\rbrack}} \right)^{2}}{\sum{x\lbrack m\rbrack}^{2}}} & (13)\end{matrix}$

i.e., the squared “distance” between the transient candidate and theexemplar normalized to the energy of the exemplar.

The lower the value of S, the better the match between the two signals.As such, a threshold level may be set below which the signals may becategorized as matching. In embodiments, the threshold level may be setempirically.

For all seventh-harmonic transients corresponding to any event type, andall frequency transients corresponding to ECU on-events and both ECU andgenerator off-events, this threshold was set to S_(o)=0.2. For generatorturn-on events, where the frequency transients are less distinct, thisthreshold was S_(o)=0.45. If the algorithm determines that the transientmatches the exemplar, it recursively updates the exemplar as,

x[m]=αx[m]+(1−α)y[m+I _(o)].   (14)

In one illustrative embodiment. the recursive update factor, α, is setto 0.8.

Each of the two stream's transients of each event are evaluatedindependently, meaning they can both match their exemplar and becombined as illustrated in block 74 of FIG. 16, one can match and becombined (blocks 76, 78 of FIG. 16), or neither match (block 80 of FIG.16). When a transient does not match a previous exemplar or no previousexemplar exists, the technique sets the transient as a new exemplar.Some events, e.g., a generator turn-off under low loading (FIGS. 9-9B),do not create significant transients in the frequency stream. For theseevents, only the seventh-harmonic transient is used in exemplarcreation. Using this technique reduces the number of total frequency andin-phase seventh harmonic exemplars from 279 of each to 56 and 64,respectively.

With these exemplars, one can identify (i.e. on-line transientdetection) “fresh” transient events by first detecting potential eventsin the frequency and seventh harmonic streams and then matching themagainst the created exemplars.

In one embodiment, the rate of event generation and the homogeneity ofthe large ECU loads allows a change-of-mean detector to perform well asa transient detector, although other choices are possible. In thefrequency stream, the detector classifies a deviation exceeding 200 MHzfor more than 4 ms, or by more than 10 MHz for more than 2 s, as apotential transient. These criteria were chosen based on the observednoise variance and the magnitudes of the frequency transients caused byevents of interest.

In the seventh harmonic stream, an edge corresponding to a step greaterthan 0.1V is considered a transient for the loads. To detect an edge, inone example embodiment, the transient detection algorithm smooths the

_(7,r)[m] stream using a filter (e.g. a low pass filter such as aGaussian low-pass filter with a standard deviation of 0.3 s), and thenevaluates the first difference of the result, s[m], as,

$\begin{matrix}{{d\lbrack m\rbrack} = {\frac{{s\lbrack m\rbrack} - {s\left\lbrack {m - 1} \right\rbrack}}{{t_{z}\lbrack m\rbrack} - {t_{z}\left\lbrack {m - 1} \right\rbrack}}.}} & (15)\end{matrix}$

A local maximum in d[m] exceeding three times its standard deviationevaluated across the exemplar window is empirically set to indicate anedge. The identification process then assesses the step size byaveraging the values of

_(7,r)[m] for 1.5 s before and after the detected edge, and classifiesthe step as a transient if the difference of these averages exceeds the0.1V change in magnitude cutoff.

Once an event is confirmed, the identification technique compares themeasured transient to all event exemplars corresponding to the estimatedgenerator configuration and base load. This estimation is based onpreviously identified transients and/or the level of

_(7,r) (FIGS. 12 and 13).

This matching technique, illustrated in FIG. 17, follows a process whichmay be the same as or similar to that used in exemplar creation and mayuse the same threshold values.

Measured transients are compared with the exemplar transients of eachevent type. As depicted in the ECU turn-on event category (block 82 inFIG. 17), event types may contain multiple exemplars for one or both ofthe two streams. In these instances, the match score of the measuredtransient may be calculated or otherwise determined for each exemplarytransient and the score indicating the best match is taken. In the caseof a generator turn-off under low loading conditions, there is nofrequency exemplar to compare against, and the frequency transient scoreis instead determined as the scaled RMS value of its deviation,

$\begin{matrix}{S = {\sqrt{\frac{\sum{\Delta\;{\hat{f}\lbrack m\rbrack}^{2}}}{E_{o}}}S_{o}}} & (16)\end{matrix}$

Here, E_(o)=2.4, which is the signal energy of a frequency deviation of10 MHz for 2 seconds (240 m-samples), i.e., the larger energy value ofthe frequency transient detection thresholds described in the transientdetection section. So is set to 0.2, the matching threshold forgenerator turn-off frequency transients. If these match scores are belowthe threshold values for both the frequency and seventh harmonic streamsof a particular event type, the transient is identified as occurring dueto that event type. If no event is identified after this process, thedetected transient is deemed due to a non-ECU or generator event and isignored.

Simultaneous or near-simultaneous events create “collision transients.”Collision transients can be caused by the actions of any device,including ECUs, generators, or pumps. They are not in general repeatableor distinct because the exact timing of the events that cause them tohave a large effect on the conglomerate shape and extent of thetransient. Collision transients, while rare, are therefore currentlyunidentifiable. It is noted that reconciling simultaneous events couldbe important, and point to two possibilities for maintaining situationalawareness in the presence of collision transients. First, the“collision” may be resolvable as the sum of known exemplars. Second, itmay not be necessary to resolve the exact details of the collision. Foran autonomous ECU control algorithm, it may be sufficient to know that acollision has occurred, and develop a set of operating rules forresolving and moving past the collision with individual control efforts.

Next described are Autonomous “Self-Driving” Electrical Loads, alsoreferred to herein simply as “autonomous loads.”

After reading the disclosure provided herein, it is understood by one ofordinary skill in the art that the systems, and techniques describedherein are applicable to residential, commercial, and industrialsections of a conventional utility, particularly a subsection such asthat fed by a distribution transformer. It is further understood by oneof ordinary skill in the art that, although a particular form ofinformation network is described hereinbelow, such description is madesimply for the purpose of providing clarity to the broad concepts soughtto be protected and that any information network can be used to exchangeload operation information, including the internet, wireless signals,power line carrier signals, dedicated communication networks of manywell-known types, and so forth.

Furthermore, in the illustrative embodiments described herein, the“dual-use” of power wiring to convey both power and information throughpower quality or voltage waveform distortions is demonstrated. Thesevoltage waveform distortions will be available on a microgrid, but couldalso be examined on sections of any power utility, including onresidential, commercial, and industrial power systems, grids on ships,aircraft, transportation systems, and so on. The approach here can beapplied to both AC and DC utilities.

Referring now to FIG. 18, shown is a pot of energy consumption for anillustrative system which may be the same as or similar to one of thesystems described above in conjunction with FIGS. 1, 4 and 5. Forpurposes of illustration, the electrical demand of the system ischaracterized by three loads. For example, load 1 may correspond to aheating system, load 2 may correspond to a ventilation system and load 3may represent the collective energy consumption of the remainder of theelectrical loads in the system. As may be expected, in cold weather, thesystem energy demand is dominated by the load 1 (i.e. heaters). As shownin FIG. 18, in this illustrative embodiment, during heating operation,the heaters account for over 70% of the total base load. It should, ofcourse, be appreciated that under other environmental conditions, thesystem energy demand may be dominated by a different load. For example,in cases of extreme heat, the system energy demand may be dominated byhigh voltage air conditioning (HVAC) components.

Given the example scenario of FIG. 18 and referring now to FIG. 19,shown is a plot which illustrates system power draw vs. time.Observation of this plot reveals the opportunity to reduce energyexpenditures of the system through autonomous load coordination. FIG. 19shows the power draw of the system over a two hour window. Region 90represents the total non-heater load, while region 92 represents thecumulative heater load. As can be seen from FIG. 19, during this twohour time period, the non-ECU heater load was stable, but the cumulativeheater load varied significantly and often as the number ofsimultaneously operating heaters varied. The average total load duringthis period was 44 kW compared to the peak load over 70 kW.

These peaks are caused by the conventional thermostatic control employedby the heaters to regulate system temperatures.

In response to the system reaching a first system temperature threshold,each heater deactivates (i.e. turns off). With the heater off, thesystem temperature may decrease until reaching a second systemtemperature threshold. In embodiments, the first system temperaturethreshold corresponds to a temperature which is higher than the secondsystem temperature threshold. For example, the first system temperaturethreshold may correspond to a relatively high system temperature and thesecond system temperature threshold may correspond to a relatively lowsystem temperature. When the system temperature reaches the secondsystem temperature threshold, the heater re-activates (i.e. turns on andthus generates heat).

Thus, the above-described system operation is similar to the behavior inmost residential or commercial venues, in that the heaters andassociated heater control systems receive no coordination. Rather, theheaters (and associated heater control systems) simply respond totemperature conditions in the structure that they serve. The “random”timing of each heater activations leads to a sliding phase of operationwith respect to other heaters in the system. With such lack ofcoordination, the possibility exists that periodically, many heaters(and possibly all heaters) will be “on” at the same time, putting amaximum demand on the grid (which may be a microgrid) from which theheaters draw power.

As shown in FIG. 19A, this operating profile is unfortunate andunnecessary as it may require multiple power sources (e.g. multiplegenerators) to support the grid where if coordination were used fewerpower sources (e.g. even a single power source such as a singlegenerator) could be used. Thus, lack of coordination results in lessefficiency. For example, if the power sources are generators whichoperate on fuel (e.g. an oil-based fuel such as gasoline or dieselfuel), lack of coordination between loads may results in less efficientfuel use.

Except under the most extreme environmental conditions, no environmentalcontrol system (e.g. a heating system or an HVAC cooling system) needsto operate continuously without pause. The operation of multipleenvironmental control systems could instead be interleaved to reduce(and ideally minimize) peak demand while maintaining occupant comfort.

In accordance with the concepts described herein a coordinated controlapproach could be used to make systems more efficient while maintainingoccupant comfort. For example, in the illustrative scenario described inconjunction with FIG. 19, no more than three out of four heaters (e.g.loads 52 a-52 d in FIG. 5) need operate simultaneously. In this example,the use of coordinated control would reduce the aggregate load such thatthe load could be met by a single power source (e.g. a single generator)resulting in an efficiency improvement of about 18%.

Using such a control technique an electrical utility or other operatorcould specify a maximum number of loads (e.g. active ECUs), that couldbe operated during a given operating window of time for the utility.Well-behaved ECUs could be made aware of the operation of other ECUs andthus could wait for their turn to become operational. For example, whenone ECU turns off another ECU could turn on. This approach could be usedto schedule needed services (e.g. environmental services such as heatingor cooling services) to operate at a proper time thereby reducing (andideally minimizing) peak power demand in a global operating schedulewithout requiring any special network or specially coordinated controlacross the different loads (e.g. without requiring any special networkor specially coordinated control across different ECUs).

In some systems, such autonomous coordination could mean the differencebetween running efficiently with one generator (or more generally onepower source), or wasting fuel with multiple inefficiently loadedgenerators operating to meet occasional peak demand. On a conventionalelectric utility, this type of coordination across residences, forexample, could mean the difference between operating or overloading adistribution transformer.

To provide autonomous loads capable of operating in accordance with theconcepts of the above describe control scheme, each load may deduceinformation about the states of the other loads on the grid. Thisinformation could be shared by any of several networking schemes,including wired networks, wireless networks, optical networks, or powerline carrier communication networks.

An alternative approach is to create awareness through the observationof the utility voltage waveform itself, with no other additional orparticular information exchange. In this case, the utility may serve asboth the source of power and also as a signaling network. This approachworks particularly well on microgrids or sections of a conventionalutility where the voltage regulation is not perfectly “stiff.” Describedabove in conjunction with FIGS. 1-17 is an event identificationtechnique that permits a load to nonintrusively recognize the operationof neighboring loads by identifying power quality changes in the voltagewaveform shared by the loads. As described above, this event detectiontechnique requires no special control wiring or networking.

FIGS. 20-20B depict the operation of an event identifier.

Referring now to FIG. 20, a plot which illustrates an event identifieroutput as it tracks the number of loads (e.g. ECU heater loads) onlineand the number of power sources (e.g. diesel generators) dispatched. Ascan be seen, with 4 on-line loads (e.g. 4 online ECUs) 2 generators arealso online. However, when the number of on-line loads decreases to 3from 4 (e.g. the number of online ECUs decrease to 3 from 4), there isno change in the number of generators which are online (i.e. 2generators remain online).

Referring now to FIG. 20A, a plot which illustrates the frequency of asystem operating with a microgrid which receives power from multiplegenerators at 120/208V at 60 Hz shows the frequency of the microgridutility nominally near 60 Hz. However distinct deviations 96, 98 areshown as the generators and loads (e.g. ECUs)in the system activate anddeactivate. These frequency deviations occur because the generators donot have over-sized spinning energy reserves in comparison to the loadrequirements.

FIG. 20B shows the measured in-phase 7th harmonic content in the systemsmicrogrid voltage. This voltage distortion is due to the powerelectronics of ECU fan drives and the space harmonics in the rotatingmachinery of the generators, and its level changes as loads andgenerators come on and off-line. Together, the frequency variations andchanges in seventh harmonic content provide “fingerprint signatures”useful for distinguishing critical events, including the turn-on andturn-off of ECUs and the addition or removal of a generator from thegenerator pool.

Specifically, in FIGS. 20A and 20B, the small frequency fluctuation(FIG. 20A) and large 7th harmonic step change (FIG. 20B) atapproximately 204 mins corresponds to a generator turn-off event. Later,the positive frequency spike/small negative 7th harmonic step andnegative frequency spike/small positive 7th harmonic step correspond toECU heater turn-off and turn-on events, respectively.

Generally, the ability of a controller to recognize these signatures asidentifiable events can be approached as a machine learning problem.However, given limited labelled event data, the signal-processingapproach for transient recognition described above in conjunction withFIGS. 1-17 may be used. As described above, this signal-processingapproach is based upon the cross-correlation between a representativeevent transient, (also referred to as a “representative event transientsignature” or more simply as an “exemplar”), and a detected event. Underthis process, an event is detected via analysis of the amplitude andtime-length of frequency deviation and the amplitude in the step changein the 7th harmonic. For any system, threshold values defining an eventmay be empirically defined. For the example system described hereinabove, threshold values defining an event were empirically defined asany of the following: a frequency deviation of 200 MHz or more forlonger than 4 ms, a frequency deviation of 10 MHz or more for longerthan 2 s, or a 7th harmonic step change of larger than 0.1V.

Once a candidate event is identified, segments of the measured frequencyand 7th harmonic data streams containing the event are compared againstfrequency and 7th harmonic exemplars for each of the following events,an ECU turn-on, an ECU turn-off, a generator turn-on, and a generatorturn-off. This comparison is quantified with a matching score which maybe defined as:

$\begin{matrix}{S = {\frac{\sum_{n = 0}^{N - 1}\left( {{x\lbrack n\rbrack} - {y\left\lbrack {n + l_{o}} \right\rbrack}} \right)^{2}}{\sum_{n = 1}^{N - 1}{x\lbrack n\rbrack}^{2}}.}} & (17)\end{matrix}$

Here, x[n] is an exemplar waveform (either frequency or 7th harmonic) ofsample-length N, and y[n+I_(o)] is an equivalent-length section of thecorresponding data stream sample-shifted by:

$\begin{matrix}{l_{o} = {\underset{l}{\arg\;\max}{\sum{{x\lbrack m\rbrack}{{y\left\lbrack {m + l} \right\rbrack}.}}}}} & (18)\end{matrix}$

Combined, Equation (18) ensures the two waveforms are time-aligned byidentifying the sample-delay, I_(o) which maximizes the correlationbetween the two signals, and (1) scores the extent to which the twowaveforms match. A perfect score, S=0, occurs when the two signals areidentical. Maximum scores are empirically defined. Below the maximumscore a measured transient is deemed to match the exemplar and above themaximum score a measured transient is deemed to not match the exemplar.

In the example system described above, the maximum thresholds are set toS_(max)=0.2 for all seventh-harmonic transients corresponding to anyevent type and all frequency transients corresponding to all event typesexcept generator turn-on events. For these, the maximum score thresholdwas set to S_(o)=0.45 as these frequency transients are less distinctcompared to those of the other events.

Given the ability to recognize the turn-on and turn-off events ofneighboring loads, as will be described in detail below, a new kind ofcontroller (referred to herein as an autonomous load controller) can beprovided to accompany each load. Such an autonomous load controller canautomatically respond intelligently to the behavior of other loads inorder to reduce (and ideally minimize) utility demand. The autonomousload control techniques described herein work with the above describednonintrusive event identification technique, though the autonomous loadcontrol techniques can also be used with loads that communicate bydedicated networks or any other convenient means.

Referring now to FIG. 21 a system in which like elements of FIG. 4 areprovided like reference designations, includes a bank of power sourceswhich may comprise one or more power sources 40A-40N coupled to grid 42.

In this illustrative embodiment, power source bank 40 is here shown toinclude M generators denoted 40 a-40M and thus may be referred to as agenerator bank. It should, of course, be appreciated that power sourcebank 40 may include any number of power sources (e.g. one or more powersources) and that the power sources may be any type of electrical powersource including but not limited to fuel operated generators, solargenerators, wind power turbines, to name but a few different types ofpower sources. It should also be noted that at any point in time, one,some or all of the M power sources may act as a source of electricalpower and this provide electrical provide service to grid 42. It shouldalso be noted that grid 42 may correspond, for example, to a portion ofa larger electrical grid system or may correspond to a microgrid.

Also couple to grid 42 are one or more loads including one or moreautonomous loads 100 a-100N (generally referred to as autonomous loads100). Although not explicitly shown in FIG. 21, it should be appreciatedthat other loads (generally denoted 102) which are not autonomous loadsmay also be coupled to grid 42. Such loads 102 are sometimes referred toherein as unmonitored loads 102. Autonomous loads 100 and unmonitoredloads 102 coupled to grid 42 are both capable of receiving power via thegrid. It should be noted that at any point in time, one, some or all ofthe loads 100, 102 may draw power from grid 42. Load 101 a may beprovided, for example, as a cyclically operating load (i.e. load whichsporadically or periodically draw power from the grid) and a high demandload (i.e. a load, which when operating, draws an amount of power fromthe grid which is relatively large compared with the amount of powerbeing drawn by other loads on the grid or which is relatively largecompared with the amount of power available from the grid).

Taking autonomous load 100 a as representative of all N autonomousloads, load 101 a has coupled thereto a detection circuit 102 a.Detection circuit 102 a may be configured to measure, sense or otherwisedetect a characteristic of a signal provided to the autonomous load 100a. For example, Detection circuit 102 a may be configured to measure,sense or otherwise detect a characteristic of a signal provided to load101 a (which may be one or more analog signals or one or more digitaldata values). In embodiments, the detection circuit is configured tomeasure, detect or otherwise sense one or more of: a power signal, avoltage signal or a current signal. In embodiments, the one or moresignals measured, detected or otherwise sensed by the detection circuitmay be one or more an analog signals, one or more digital signals or acombination of one or more analog and digital signals. In embodiments,the detection circuit may be configured to measure, detect or otherwisesense signal characteristics including but not limited to: signalfrequencies and/or amplitudes; changes in signal frequencies and/oramplitudes, signal waveforms; changes in signal waveforms; power levels(e.g. minimum, average and/or peak power levels); changes in powerlevels; signal transients (including characteristics of amplitude andfrequency transients); and/or changes in signal transients.

Detection circuit 102 a is coupled to an event identification processor103 a. Event identification processor receives one or more signals(which may be one or more analog or digital signals) from detectioncircuit 102 a and extracts and identifies a load event based upon thesignal measured, sensed or otherwise detected by the detection circuit101 a.

Identification processor 103 a processes the signals provided thereto toidentify load operation characteristics or “signatures” one or more ofthe respective loads 100 a-101N and 102 using any of the techniquesdescribed above in conjunction with FIGS. 1-20.

Identification processor 103 a is coupled to provide information to anautonomous load controller 104 a and thus processor 103 a providesinformation to autonomous load controller 104 a. Autonomous loadcontroller 104 a utilizes the information provided there to enableautonomous control of load 100 a. Thus, autonomous load controller 104 amay be configured to control at least some operation of a load to whichit is coupled (e.g. load 100 a in the illustrative embodiment of FIG.21). That is, autonomous load controller 104 a is configured to receiveload event information from the event identification processor 103 a andprovide a control signal to the load 100 a in response to the load eventinformation.

In embodiments, the autonomous load 100 a may further comprise acommunication system 105 a coupled to load controller 104 a.Communication system 105 a operates to allow communication between atleast the various autonomous loads 100 a-100N and in some embodimentswith other systems such as other control systems. In embodiments,communication system 105 a may be implemented as power line carriercommunication system, a wire-based communication system (e.g. telephonenetworks, or ethernet networks, and fiber-optic networks, waveguidetransmission lines), or wireless communication system (e.g. a cellulartransceiver or other type of transceiver for performing packet-basedcommunication). Other forms of signaling may also be used. Thus,communication system 105 a may be used for signaling or otherwisecommunicating with other autonomous loads or other systems such as othercontrol systems.

In this case (i.e. when the autonomous load controller 104 a is coupledto a load) the load may be referred to as an “autonomous load” 100 a oras a “self-driving load” 100 a (i.e. a load which utilizes signatureinformation generated by a transient identification processors and usesthe signature information to operate in accordance with an autonomouscontrol scheme). Examples of autonomous loads will be described indetail below in conjunction with FIGS. 22-27.

As noted above in conjunction with FIG. 1, in embodiments, one or moreloads 100 a may be conventional electrical loads having a separatevoltage detection circuit and transient identification processor coupledthereto (i.e. one or both of voltage detection circuit and transientidentification processor may be separate from the load). In embodiments,one or more loads 100 a may have the voltage detection circuit andtransient identification processor provided as an integral part ofthereof. Likewise, in embodiments, autonomous load controller 104 a maybe provided as an apparatus (e.g. a circuit, device or processor) whichis separate from a load, the voltage detection circuit and the transientidentification processor. In other embodiments, however, autonomous loadcontroller 104 a may be provided as an apparatus (e.g. a circuit, deviceor processor) which is integral with or provided as part of. one or moreof a load, a voltage detection circuit and a transient identificationprocessor

One example of a load which may be provided with or coupled to anautonomous load controller is an environmental control system. Anenvironmental control system which includes an autonomous loadcontroller (sometimes referred to herein as an autonomous environmentalcontrol system) may be appropriate for use in a variety of differentstructures including, but not limited to, an office buildings, anresidential apartment buildings, hotels, motels, single or multi-familyresidences or any other type of structure in which it is desired tomaintain an ambient temperature at a particular temperature or within aparticular range of temperatures.

Under the autonomous control technique described herein, each autonomousload controller in an autonomous environmental control system mayconstantly or periodically monitor the temperature of the structure withwhich it is associated (e.g. the temperature of the structure and/or itslocal voltage, processing the latter for frequency and 7th harmonic datastreams). Both the temperature and voltage quality information can“trigger” one or more interrupt service routines (ISRs) or other type ofprocessing, which updates the autonomous load controller knowledge (andthus also necessarily updates the autonomous load knowledge of the gridstate and/or cause the load (e.g. a heater) to change states.

This, autonomous loads 100 a-100N are capable of changing theirrespective operating states in response to load event information. Forexample, in response to receiving load event information which indicatesthat another load coupled to the same electrical grid as the autonomousload has changed its state (e.g. the other load has turned on, turned,off, slowed down, sped up, is drawing more or less power from a grid,etc. . . . ) the autonomous load can change its state accordingly. Forexample, in response to an autonomous load receiving load eventinformation which indicates that another load has changed its state fromoff to on, the autonomous load may change its state from on to off. Inthis example, the autonomous load may change its state to off to preventdrawing more power than can be sourced by the electrical grid to whichboth loads are coupled.

It should be appreciated that in embodiments detection circuit 102 a maybe provided as a voltage detection circuit which may be the same as orsimilar to voltage detection circuit 46 a described above in conjunctionwith FIG. 4. Similarly, event identification processor 103 a may beprovided as a transient identification processor 48 a which may be thesame as or similar to transient identification processor 48 a describedabove in conjunction with FIG. 4. Thus, in embodiments, processor 103 amay provide transient-related information to autonomous load controller104 a which enable autonomous control of load 100 a. Thus, autonomousload controller 104 a may be configured to control at least someoperation of a load (e.g. load 100 a) to which it is coupled.

Referring now to FIG. 22, for illustrative purposes, a state diagram ofan example load corresponding to a heater is shown. Under traditionalthermostatic control, the heater only changes states in response to thetemperature of a system (or a physical space) which it is heating.Specifically, when off (i.e. when in state 108), the heater will turn ononly when the temperature inside the system decreases below a lowertemperature threshold, T_(l), and when on (i.e. when in state 109), theheater will only turn off when the temperature rises above an upperthreshold, Th. While simple and robust, this type of control creates thelarge aggregate demand peaks depicted in FIG. 19, which can lead toinefficiencies and excess strain on grid distribution equipment.

It should be noted that autonomous loads may be described as having aself-distributing characteristic. That is, autonomous loads haveinformation (or have access to information) concerning their own stateas well as the states of one or more other loads coupled to the grid 42(e.g. one or all of autonomous loads 100 or unmonitored loads 102) orinformation concerning grid 42, and are configured to operate in amanner which is beneficial to the overall system such as the examplesystem illustrated in FIG. 21 (i.e. an autonomous load is aware andresponsive to the aware signal to reduce, or ideally minimize, aggregatepeaks in an electrical power system). For example autonomous loads maybe configured to avoid peaks or stated differently, the autonomous loadsmay “self-distribute” their loads (e.g., automatically change the amountof power they draw from the grid) to avoid peaks. As explained above,the may be accomplished, for example by one or more autonomous loadsautomatically turning on or off). Thus, an autonomous heater asdescribed in FIG. 22, for example, can maintain the temperatureresponsiveness of a traditional thermostatic control, but also improvesthe collective behavior of other loads coupled to the grid by limitingthe number of simultaneously operating heaters. This is accomplished viathe use of an autonomous load control technique.

Using a heater comprising an autonomous load controller as an example ofan autonomous load, Table I lists processes (e.g. interrupt routines)for each heater state, in order of “precedence,” along with the triggerevent that causes their operation.

TABLE I State Interrupt Service Routine Trigger Heater off 1. Transientidentification Transient event detected 2. Under-temperature T_(t) <T_(l) Heater on 1. Transient identification Transient event detected 2.Over-temperature/ T_(t) > T_(h) OR t_(on) > t_(on, max) Overlength

Notably, in both states 108, 109 (i.e. both the heater on and heater offstates), the transient identification routine takes precedence over thetemperature-based routines. That is, if a transient event is detected(triggered) while the heater autonomous load controller is executing atemperature-based routine, the autonomous load controller will abort thetemperature-based routine and execute the transient identificationroutine before reinitiating the temperature-based routine.

Next described are example temperature based routines.

Referring now to FIG. 23, and considering first an under-temperatureprocess (which may be implemented as an ISR, for example) FIG. 23depicts an illustrative operational process for an under-temperatureroutine which triggers when T_(t)<T_(l). where Tt is the systemtemperature (e.g. the temperature of a room) and T_(l) is the lowertemperature threshold. However, unlike traditional thermostatic control,this condition alone only initiates the routine rather than ensures theheater turns on.

Under the autonomous control scheme, as shown in decision block 110, theautonomous load controller considers the operation mode of the heater.In this example, the two operating modes of the heater are startup,i.e., when the heater is first turned on to regulate the temperature ofa space or structure to which the heater is exposed (in contrast to theoperation state of a heater heating element, for example), and normaloperation, which the heater switches to after first eclipsing the lowertemperature threshold, Th.

If in decision block 110, a decision is made that the system isoperating in normal mode or the temperature being controlled (e.g. aroom temperature) has dropped below a second, lower thresholdtemperature (T_(l2)) where T_(l)<T_(l2) then processing proceeds todecision block 112.

If in decision block 112 a decision is made that the number of otherheaters in their on state, N_(h), is less than a maximum number,N_(max), then processing proceeds to processing blocks 116, 118 and theheater can turn on following a delay (116). If in decision block 112 adecision is made that the number of other heaters in their on state,N_(h), is greater than a maximum number, N_(max), then processingproceeds to processing block 120 and the heater remains off. Thus, theheater can turn on so long as doing so will not cause the total numberof “on” heaters to exceed a maximum number, N_(max).

If in decision block 110, a decision is made that the system isoperating in startup mode, or if the system temperature is above orequal to its lower temperature threshold (T_(f)≥T_(l2))then processingproceeds to decision block 114. If in decision block 114 a decision ismade that the number of other heaters in their on state, N_(h), is lessthan the total number of available heaters which are authorized to beon, N_(p), processing proceeds to processing blocks 116, 118 and theheater can turn on following a delay.

If in decision block 114 a decision is made that the number of otherheaters in their on state, N_(h), is greater than the total number ofavailable heaters which are authorized to be on, N_(p), processingproceeds to processing block 120 and the heater remains off.

If in decision block 110, a decision is made that the heater isoperating in its normal mode and the temperature being controlled (e.g.a room temperature) has dropped below a second, lower thresholdtemperature (t_(l2)) such that T_(t)<T_(l2) then processing proceeds todecision block 112.

The use of a lower temperature threshold T_(l2) allows the heater toautonomously “shed load,” prioritizing limiting the aggregate heaterpower demand, until the system temperature drops to a minimum level(e.g. in the case of a heater heating a space, the minimum levelcorresponds to a minimum comfort level of an occupant in the space). Atthat point, the heater prioritizes thermal comfort and will turn on theheater so long as doing does not risk exceeding the total availablegeneration capacity of the power sources (e.g. the total availablegeneration capacity of power sources 40 in FIG. 21) In embodiments, themaximum number of heaters which are authorized to be on, N_(max), isselected based upon the number of available power sources and theirtotal capacity.

Importantly, prior to turning its heater on, the autonomous loadcontroller enacts a delay, with the length of the delay dependent uponthe system temperature error (T_(e)), which may be computed as:

T _(e) =T _(t) −T _(s),   (19)

where:

T_(t) is the system temperature; and

T_(s) is the target temperature setpoint for the system.

This time-dependent delay is may be determined as shown in equation(20):

$\begin{matrix}{t_{d} = \left\{ {\begin{matrix}{{t_{o} + {mT}_{e}},} & {{{if}\mspace{14mu} T_{e}} < \frac{t_{m\; i\; n} - t_{o}}{m}} \\{t_{m\; i\; n},} & {otherwise}\end{matrix}.} \right.} & (20)\end{matrix}$

Here, t_(o), m, and t_(min) are positive values representing the offsetand slope of the delay's relationship with temperature error in itslinear region, and an optionally set minimum temperature delay,respectively.

The over-temperature l overlength process as shown in FIG. 24 (which maybe, for example, an ISR) enacts control similar to thermostatic controlin response to the system temperature eclipsing a high temperaturethreshold level, though the routine also imparts an error-dependenttime-delay,

$\begin{matrix}{t_{d} = \left\{ {\begin{matrix}{{t_{o} + {mT}_{e}},} & {{{if}\mspace{14mu} T_{e}} < \frac{t_{m\; i\; n} - t_{o}}{m}} \\{t_{m\; i\; n},} & {otherwise}\end{matrix}.} \right.} & (21)\end{matrix}$

It should be appreciated that the purpose of the delay timer in both ofthese temperature-based routines (i.e. in FIGS. 23 and 24) is to allowthe heater most in need to take action first.

One final note regarding the over-temperature l overlength ISR is thatit is also triggered if the time that a heater is on, t_(on) exceeds amaximum threshold, t_(on,max). This rule enforces resource sharing amongthe autonomous loads. Similar to the delay-times, the maximum on-timecan be made dependent on temperature error to promote increased resourcesharing allocation to the tents that most require heat.

Referring now to FIG. 25, a transient identification routine istriggered when an event is detected in either or both of the frequencyor 7th harmonic data streams. If an event is identified as a powersource or heater on/off event 124 according to its cross-correlationscores 122, the routine updates the heater autonomous load controller'sknowledge of the microgrid state accordingly (126, 128). When the heaterdoes not recognize the event, it is either because the event was causedby one or more unmonitored loads, or because a heater or a power sourcechanged states at nearly the same as did another heater, power source orunmonitored load. This situation may be referred to as an “eventcollision” 130. To address (and ideally rectify)such an ambiguity, eachheater may calls one of two collision-handling routines 132, 134,depending upon if it participated in the collision (i.e. it changedstates at the time of the unidentified event) or just observed it. Twoexample routines are described below in conjunction with FIGS. 26 and27.

The function of the illustrative collision-handling routines shown inFIGS. 26 and 27 is to reverse all controllable actions which contributedto the collision, and to determine whether any uncontrolled events ofinterest (power source turn-on or turn-off events, in this case) alsocontributed.

Referring now to FIG. 26, for a participant in the collision, processingbegins as shown in processing block 140 by the participant logging theaverage value of their 7th harmonic streams for a period of time (e.g.one second) preceding the unidentified transient. The period of time maybe preselected, or adapted or selected in the field based on how oftentransient events typically occur, and therefore establishing a likelytime when the indicator, seventh harmonic in this example embodiment, isfree of transient activity from the loads that caused the collision.

Following this, processing proceeds to processing block 142 in whichcollision participants reverse their actions (e.g. heaters that turnedon, turn back off) and update their N_(h) counts to comply with thereversal. Significantly, this action also restores the accuracy of theother autonomous heaters as they will not have recognized the collisionevent. Once this reversal concludes (assessed by observes either bywitnessing the reversal or after a set length of time), processingproceeds to processing block 144 in which each heater in the collisionmeasures (via the transient identification processor) the 7th harmonicstream again for comparison with pre-collision levels.

Because any heaters involved in the collision have reversed theiractions, there is no difference in the number of heaters operatingduring these two measurements, and only uncontrolled loads or powersource events will have caused differences in the 7th harmonicmeasurements.

As discussed, in embodiments, the effect of power source events on the7th harmonic may be larger (and in some cases, significantly larger)than those of uncontrolled events, and thus as shown in decision block146, the autonomous load controller determines power source eventparticipation based upon the size of the difference between thepre-transient and post-transient 7th harmonic measurements. Processingthen proceeds to either processing block 148 (generator event) 150(unmonitored load) based upon the size of the difference between thepre-transient and post-transient 7th harmonic measurements.

Referring now to FIG. 27, for an observer of the collision, processingbegins as shown in processing block 140 by the participant logging theaverage value of their 7th harmonic streams for a period of time (e.g.one second) preceding the unidentified transient.

Processing then proceeds to processing block 143 in which collisionobservers await confirmation that collision participants have reversedtheir actions (e.g. heaters that turned on, turn back off) or collisionobservers simply wait a predetermined period of time. Once the reversalconcludes (assessed by observes either by witnessing the reversal orafter a set length of time), processing proceeds to processing block 144in which each heater measures (via the transient identificationprocessor) the 7th harmonic stream again for comparison withpre-collision levels.

As above explain above in conjunction with the processing of FIG. 26,because any heaters involved in the collision have reversed theiractions, there is no difference in the number of heaters operatingduring these two measurements, and only uncontrolled loads or powersource events will have caused differences in the 7th harmonicmeasurements.

As also discussed above, in embodiments, the effect of power sourceevents on the 7th harmonic may be larger (and in some cases,significantly larger) than those of uncontrolled events, and thus asshown in decision block 146, the autonomous load controller determinespower source event participation based upon the size of the differencebetween the pre-transient and post-transient 7th harmonic measurementsand processing proceeds to either processing block 148 (generator event)150 (unmonitored load) based upon the size of the difference between thepre-transient and post-transient 7th harmonic measurements.

It should be understood that various embodiments of the conceptsdisclosed herein are described with reference to the related drawings.Alternative embodiments can be devised without departing from the scopeof the broad concepts described herein. It is noted that variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. As an example of an indirectpositional relationship, references in the present description todisposing a layer or element “A” over a layer or element “B” includesituations in which one or more intermediate layers or elements (e.g.,layer or element “C”) is between layer/element “A” and layer/element “B”as long as the relevant characteristics and functionalities oflayer/element “A” and layer/element “B” are not substantially changed bythe intermediate layer(s).

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance, or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “one or more”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include an indirect “connection” and a direct “connection”.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedcan include a particular feature, structure, or characteristic, butevery embodiment can include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

For purposes of the description provided herein, the terms “upper,”“lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” andderivatives thereof shall relate to the described structures andmethods, as oriented in the drawing figures. The terms “overlying,”“atop,” “on top,” “positioned on” or “positioned atop” mean that a firstelement, such as a first structure, is present on a second element, suchas a second structure, where intervening elements such as an interfacestructure can be present between the first element and the secondelement. The term “direct contact” means that a first element, such as afirst structure, and a second element, such as a second structure, areconnected without any intermediary conducting, insulating orsemiconductor layers at the interface of the two elements.

One skilled in the art will realize the concepts, structures, devices,and techniques described herein may be embodied in other specific formswithout departing from the spirit or essential concepts orcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of thebroad concepts sought to be protected. The scope of the concepts is thusindicated by the appended claims, rather than by the foregoingdescription, and all changes that come within the meaning and range ofequivalency of the claims are therefore intended to be embraced therein.

What is claimed is:
 1. A system for identifying a load event on anelectrical grid, the system comprising: (a) voltage detection circuitcoupled to measure a voltage signal at an input of a load and to providea measured voltage signal at an output thereof; (b) a transientidentification processor configured to process the measured voltagesignals from the output of the voltage detection circuit to identifyline frequency variation and frequency harmonic characteristics in themeasured voltage signals and further configured to identify a load eventbased upon the frequency variation and frequency harmoniccharacteristics.
 2. The system of claim 1 wherein the transientidentification processor is configured to extract frequency and voltageharmonic transients corresponding to individual load events.
 3. Thesystem of claim 1 wherein the identified frequency harmoniccharacteristics corresponds to a 7th frequency harmonic characteristic.4. The system of claim 1 wherein the transient identification processorcomprises: (b1) means for extracting one or more frequency harmonicsfrom the received voltage signals; and (b2) means for extracting one ormore voltage harmonic coefficients from the received voltage signals;(b3) means for receiving at least one of the one or more frequencyharmonics and at least one of the one or more voltage harmoniccoefficients and identifying a load event based upon the at least one ofthe one or more frequency harmonics and the at least one of the one ormore voltage harmonic coefficients.
 5. A method for identifying a loadevent on an electrical grid, the method comprising: (a) measuring avoltage signal waveform at an input of a load; (b) detecting frequencyvariation characteristics of the voltage signal waveform; (c) detectingfrequency harmonic characteristics of the voltage signal waveform; (d)generate a load event signature from the frequency variationcharacteristics and the frequency harmonic characteristics; and (e)identifying a load event based upon the load event signature.
 6. Themethod of claim 5 wherein identifying a load comprises comparing theload event signature to one or more representative event transients loadevent signatures.
 7. The method of claim 6 wherein comparing the loadevent signature to one or more exemplar load event signatures comprisesperforming a cross-correlation between a representative event transientand the generated load event signature.
 8. The method of claim 5 whereinmeasuring a voltage signal waveform at an input of a load comprisesdigitally sampling a voltage signal waveform at an input of a load toproduce a stream of digital data samples v[n].
 9. The method of claim 8wherein detecting the frequency variation characteristic of the voltagesignal waveform comprises: estimating the times of voltagezero-crossings in the stream of digital data samples v[n]; andestimating an average frequency across an m^(th) data section of lengthN_(p) voltage periods to provide a time-series stream of frequencyestimates {tilde over (f)}[m].
 10. The method of claim 8 whereindetecting frequency harmonic characteristics of the voltage signalwaveform comprises: estimating an average frequency across an m^(th)data section of length N_(p) voltage periods to provide a time-seriesstream of frequency estimates {tilde over (f)}[m]; and generating aharmonic stream {circumflex over (V)}_(k)[m] by determining coefficientsfor the time-series stream of frequency estimates {tilde over (f)}[m].11. A system for identifying a load event on an electrical grid, thesystem comprising: (a) voltage detection circuit coupled to measure avoltage signal at the input of a load and to provide a measured voltagesignal at an output thereof; (b) a transient identification processorconfigured to receive measured voltage signals from the output of thevoltage detection circuit, the transient identification processorcomprising: (b1) means for extracting one or more frequency harmonicsfrom the received voltage signals; and (b2) means for extracting one ormore voltage harmonic coefficients from the received voltage signals;(b3) means for receiving at least one of the one or more frequencyharmonics and at least one of the one or more voltage harmoniccoefficients and identifying a load event based upon the at least one ofthe one or more frequency harmonics and the at least one of the one ormore voltage harmonic coefficients.
 12. The system of claim 11 furthercomprising: means for providing the measured voltage signals from theoutput of the voltage detection circuit as a digital data stream v[n];and wherein the transient identification processor further comprises azero crossing detector (ZCD) configured to receive the digital datastream v[n] from the voltage detection circuit and is configured toestimate the times of voltage zero-crossings in the digital data streamv[n].
 13. The system of claim 12 further comprising an interpolatormodule configured to receive a zero crossing data stream {circumflexover (t)}_(z)[m] and a digital data stream v[n] and estimate an averagefrequency across the m^(th) data section of length N_(p) voltage periodswhich wherein the zero crossing detector is configured to interpolatingbetween the voltage data points just before, v[n⁻ _(m)], and just after,v [n⁺ _(m)], the m^(th) zero crossing,
 14. A method for identifying anelectrical event on a grid, the method comprising: measuring a voltagewaveform at a load; detecting a frequency transient and a harmonictransient of the measured voltage waveform; comparing the frequencytransient and the harmonic transient to at least one signature exemplar,the at least one signature exemplar comprising a frequency transientsignature and a harmonic transient signature, the at least one signatureexemplar representing a power source event type and a load event type;computing a match score based upon the comparison of the measuredfrequency transient and the measured harmonic transient to at least oneexemplar; and identifying an event based on the computed match score.15. The method of claim 14, wherein the measured harmonic transient is aseventh harmonic.
 16. The method of claim 14, wherein the power sourceevent type includes a power source turn-on event.
 17. The method ofclaim 16, wherein the power source event type includes a power sourceturn-off event.
 18. The method of claim 14, wherein the measuredfrequency transient is a deviation in a frequency stream of the voltagewaveform of a threshold frequency for a threshold period of time.